Webinar on Open Ended Survey Analysis using Machine Learning

Webinars always play a vital role in understanding a product and the technology used behind it which will educate users to learn WHAT, WHY and HOW about any technology and product. We are excited to announce our webinar that is designed to educate users about how to do Open Ended Survey Analysis using Machine Learning. The webinars will also include the step-by-step process of WHAT, WHY and HOW about Open Ended Survey Analysis using Machine Learning.

You’re invited to join us for the webinar on 21st June 2018 at 11:00 am PST. You can click on the button below to register yourself for the webinar.

About the Speakers:

Ankit Narayan Singh, CTO, ParallelDots will kick the series off by taking you through the WHAT and WHY of the webinar on Open Ended Survey Analysis Using Machine Learning followed by Kushank Poddar, Head of Business, Karna.AI who will be taking you through HOW to do Open Ended Survey Analysis using Machine Learning.

What is Open Ended Survey Analysis?

With the recent advances in deep learning, the ability of algorithms to analyze text has improved considerably. Now analyzing digital and social media is not restricted to just basic sentiment analysis and count based metrics. Creative use of advanced artificial intelligence techniques can be an effective tool for doing in-depth research. We believe it is important to classify incoming customer conversation about a brand based on following lines:

Key aspects of a brand’s product and service that customers care about.

Users’ underlying intentions and reactions concerning those aspects.

You can read here more about Contextual Semantic Search, the Machine Learning technology behind Open Ended Survey Analysis.

In this webinar, you will learn the following things:

How ParallelDots’ sequential models perform way better than other techniques to perform text classification.

How users can automatically categorize a piece of text into their own custom categories without any training data (or labeled data).